There are a plethora of attacks that exploit the vulnerabilities in machine learning models and infer membership of certain individuals [1]}, [2]} or infer sensitive attributes such as age, ethnicity or genetic markers of given data instances, from released models in both black and white-box settings  [3]}, [4]}.
One approach to protect against such attacks is Differential Privacy (DP). DP can protect against attempts to infer the contribution of a given individual to the training set by adding noise to the computations [5]}, [6]}, [7]}, [8]}.
